code: def get_dataset(self):
      output_shapes = [
          (
              self._batch_size,
              self._image_size[0],
              self._image_size[1],
              3,
          ),
          (self._batch_size,),
          (self._batch_size, None, 4),
          (self._batch_size, None),
      ]
      output_dtypes = [tf.float32, tf.float32, tf.float32, tf.int32]
  
      for level in range(
          self._anchors.min_level, self._anchors.max_level + 1
      ):
          feat_size = self._anchors.feat_sizes[level]
          output_shapes.append(
              (
                  self._batch_size,
                  feat_size["height"],
                  feat_size["width"],
                  self._anchors.get_anchors_per_location(),
              )
          )
          output_shapes.append(
              (
                  self._batch_size,
                  feat_size["height"],
                  feat_size["width"],
                  self._anchors.get_anchors_per_location() * 4,
              )
          )
          output_dtypes.append(tf.int32)
          output_dtypes.append(tf.float32)
  
      dataset = dali_tf.DALIDataset(
          pipeline=self._pipe,
          batch_size=self._batch_size,
          output_shapes=tuple(output_shapes),
          output_dtypes=tuple(output_dtypes),
      )
      return dataset
  
file_path: DALI/docs/examples/use_cases/tensorflow/efficientdet/pipeline/dali/efficientdet_pipeline.py
imports: import nvidia.dali
  from nvidia.dali import pipeline_def
  import nvidia.dali.plugin.tf
  import tensorflow
  import math
  from absl import logging
  from glob import glob
  from pipeline import anchors
  from utils import InputType
  from .None import ops_util
label_desc: Use image node_id 5 for calling the EfficientDetPipeline obj's underlying member method code with example usage: obj.get_dataset() and returns: dataset